Augmented Cubature Kalman filter for nonlinear RTK/MIMU integrated navigation with non-additive noise

In order to enhance the capability of autonomous operation for small unmanned aerial vehicles (UAV), a MEMS-based inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation method is proposed. An augmented Cubature Kalman filter is derived to fulfil the data fusion of precise GNSS real-time kinematic (RTK) solution and noisy inertial measurements. In the filter, Cubature Kalman filtering is adopted to handle the strong INS model nonlinearity caused by sudden and large UAV maneuvers, and the technique of state-augmentation is used to capture meaningful odd-order moment information and reduce the adverse impacts of non-additive noise in inertial measurements. It is analyzed that the basic difference between the augmented and non-augmented CKFs generally favors the augmented CKF, which is supported by a representative example and flight test. The results of flight test have also shown that the proposed augmented Cubature Kalman filtering method can complete more accurate navigation compared with the conventional EKF/UKF-based approaches.